Method using ontology and user query processing to solve inventor problems and user problems

ABSTRACT

A system, method and computer program product for solving a problem based on ontology methods for data/knowledge presentation and processing, implemented in a linguistic processing module. The basic components of the linguistic processing module may be a linguistic knowledge base (KB), an ontology KB, and/or an expert KB. The problem solved may include a user or an inventor problem. The method may include storing a user query which may include a structured, or a non-structured description, parsing a non-structured query to create a structured query including a formal semantic representation of the query, semantically expanding the formal semantic representation to provide searching for relevant solutions, searching the solutions in the expert KB, and semantically sorting the solutions to produce a list of solutions of the problem.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to solution automation of inventor anduser problems, and more particularly, to using semantic methods ofinformation and knowledge representation and processing for solving suchproblems.

2. Related Art

Solving inventor problems and technical problems of a user may require,first of all, good information support, i.e. operative access toinformation or knowledge. Good information support can answer how tosolve the problem or can facilitate providing information related to thesolution of the problem (for example, information in another knowledgedomain, in the same knowledge domain but in other type of system, etc.)that is able to point to the inventor or user the needed direction forthe solution search. Conventionally, computer based informationretrieval may be performed by means of a search engine.

In unsophisticated information retrieval systems, a search may beperformed by searching for the presence of key words (inputted by theuser) in documents contained in a database. This kind of search may becharacterized by low precision and recall. Modem information retrievalsystems should provide the user the possibility of formulating a queryin natural language, i.e. the systems should have a natural languageuser interface. Then, the automatic linguistic analysis of the query isperformed and its formal representation created. The linguistic analysiscan be performed at different levels of depth of natural language. Thisanalysis, in an ideal case, should include the semantic level. It isimportant to recognize not only relations between different elements ofthe query (usually, the most informative elements), but as well therelations between query elements and the corresponding components froman outer world model or a certain knowledge domain. Thus, it becamedesirable to use semantic relations between concepts, described inmodels of knowledge representation such as a thesaurus or ontology, toimprove information retrieval system performance in different manners invarious applications.

Ontology is a hierarchical lexical structure where concepts expressed bywords or word-combinations are defined and are linked with semanticrelations. Ontologies can be domain-specific or general depending on theterms they describe and attempt to reflect a human's knowledge about thespecific domain or the surrounding world. Since ontology represents avaluable and extensive set of data, ontology can be successfully used ininformation retrieval to improve the precision and the recall of searchresults.

Some information retrieval systems like the system described in U.S.Pat. No. 6,675,159 B1 (“the '159 patent”), the contents of which isincorporated herein by reference in its entirety, use ontologies toindex collections of documents with ontology-based predicate structures.The system of the '159 patent extracts the concepts behind user queriesto return only the documents that match these concepts. The system hasthe capabilities of an ontology-based search system and it can searchfor logically structured groupings of items from the ontology. Forexample, from an exemplary query “What is the current situation of thestock market?” an attribute extractor extracts direct attributes“current”, “situation”, “stock”, and “market” from the query. Theattribute extractor can also, e.g., expand attribute “stock” to“finance”, “banks”, “brokerages”, “Wall Street”, etc. by using anontology that contains hierarchically-arranged concepts.

The information presented in a knowledge base search and retrievalsystem, described in U.S. Pat. No. 5,940,821, (“the '821 patent”) andthe related document knowledge base research and retrieval system,described in U.S. Pat. No. 6,460,034 B1, (“the '034 patent”) (thecontents of both of which are incorporated herein by reference in theirentireties) use a knowledge base (that stores associations amongterminology/categories that have a lexical, semantic or usageassociation.) for document theme vectors identification (by inferringtopics from terminology of a document), document classification incategories and are also able to retrieve a relevant document in responseto a query by expanding query terms and the theme with the help of theknowledge base. The '034 system includes factual knowledge base queriesas well as concept knowledge base queries. The factual knowledge basequeries identify, in response to a query, the relevant themes, and thedocuments classified for those themes. In contrast, the conceptknowledge base queries do not identify specific documents in response toa query, but identify the potential existence of a document bydisplaying associated categories and themes.

The content processing system of the '821 and '034 patents include alinguistic engine, a knowledge catalog processor, a theme vectorprocessor, and a morphology section. The linguistic engine, whichincludes a grammar parser and a theme parser, processes the document setby analyzing the grammatical or contextual aspects of each document, aswell as analyzing the stylistic and thematic attributes of eachdocument. Specifically, the linguistic engine generates, as part of thestructured output, contextual tags, thematic tags, and stylistic tagsthat characterize each document.

The knowledge base of the '821 and '034 patents is used to generate anexpanded set of query terms, and the expanded query term set is used toselect additional documents. To expand a query term using the knowledgebase, the levels or tiers of the classification hierarchy as well as theknowledge base associations are used to select nodes within predefinedcriteria. In one embodiment, the query term strength is decreased basedon the distance weight (e.g. query term weights are decreased by 50% foreach point of semantic distance when expanding either to a more generalcategory, (e.g. a parent category) or to an association), and all nodeswith a resultant query term weight greater than one are selected. Allchild categories and terms beneath a node are selected.

However, the system of the '821 and '034 patents is oriented mainly totheme vector identification. The '034 system requires the documents fromthe retrieved database to be indexed with special contextual, thematicand stylistic tags and the query terms expansion based on ontology isused to retrieve additional documents taking into consideration itstheme vector.

Ontology also is conventionally applied in database management systems.In International patent application publication No. WO-2003/030025A1(“the '025 publication”), the contents of which is incorporated hereinby reference in its entirety, the database management system usesontology to solve the problems of semantic heterogeneity, and semanticmismatch and query integration against distributed resources. Theproposed solution to the problems of semantic heterogeneity is toformally specify the meaning of the terminology of each system usingontologies (shared and personal ones). Thus, the system of the '025publication provides a distributed query solution for a network having aplurality of database resources. The network helps users to make querieswhich retrieve and join data from more than one resource, which may beof more than one type such as an SQL or XML database.

Consequently, ontology is used in the system of the '025 publication todisambiguate terminology vagueness while retrieving information fromdifferent heterogeneous information resources.

In U.S. patent application Publication No. 2002/0107844 A1 (“the '844publication”), the contents of which is incorporated herein by referencein its entirety, ontology is referred to be used in an informationgeneration and retrieval system as an instrument that helps to buildsemantic representation of the sentence in the form of a conceptualgraph. During the information request procedure, a natural languagequery of a user is transformed to the conceptual graph by analyzingsentence structure and semantic structure and then a conceptual graph ina database, which is nearest to the conceptual graph of the query withrespect to sense is searched and semantic appropriateness is computed todisplay information indexed by the searched conceptual graph to theuser.

Thus, the application of ontology in information retrieval impliesconceptual graph building as well as the query and database conceptualgraph comparison.

The method and apparatus for active information discovery and retrieval,described in U.S. Pat. No. 6,498,795 B1, (“the '795 patent”), thecontents of which is incorporated herein by reference in its entirety,use an active network framework and an ontology-based informationhierarchy for semantic structuring and automated information binding,and provide a symmetrical framework for information filtering andbinding in the network. Queries from information requesters are directlyrouted to relevant information sources and contents from informationproviders are distributed to the destinations that expressed an interestin the information.

The method of the '795 patent implies creating content ontology instancetrees and query ontology instance trees on each of the active networknodes. Active networks architecture and an ontology-based informationhierarchy are used as the network and the semantic frameworksrespectively. The system uses simple hypertext markup language (HTML)ontology extensions (SHOE). When a SHOE instance makes specific claimsbased upon a particular ontology, a software agent can draw on thatparticular ontology to infer knowledge that is not directly stated. Theontology provides context as implicit knowledge. SHOE tags allowdefining new ontologies based on existing ones. The search operationalmodel is applied on any part of sub-hierarchy of the ontology instancetree. Special coefficients are calculated to determine the probabilityof the child nodes of ontology to be accessed with the parent node ofontology.

Hence, ontology in the '795 patent is used for semantic structuring ofthe retrieved information, which implies previous annotation withontological tags (using SHOE, both automatically or manually) of theinformation resources, and only then it is possible to retrieveinformation based on the ontology relations represented by SHOE tags.

In U.S. patent application Publication No. 2002/0116169 A1(“the '169publication”), the contents of which is incorporated herein by referencein its entirety, a method and apparatus for generating normalizedrepresentations of strings is described. Ontologies, thesauri, andterminological databases are used therein as means for normalization ofsemantic representation of the string.

The described method of the '169 publication attempts to increase theretrieval performance of information retrieval systems by suggesting useof ontology to semantically normalize query and database strings.

An ontology-based information management system and method, described inU.S. patent application Publication No. 2003/0177112, (“the '112publication”), the contents of which is incorporated herein by referencein its entirety, uses ontology to provide semantic mapping betweenentries in a structured data source, and concepts in an unstructureddata source and includes processes for creating, validating, augmenting,and combining ontologies for life sciences, informatics and otherdisciplines. The system of the '112 publication proposes to use anontology to enable effective syntactic and semantic mapping betweenmapping entities discovered using concept-based text searching, andthose derived from data warehousing and mining in a plurality ofdisciplines.

The system of the '112 publication may evaluate the distance between apair of terms in a given information space using an informationretrieval engine capable of categorizing large document sets.

Nevertheless, the proposed method of the '112 publication is mainlyoriented to managing information sources based on ontologies, which helpto integrate structured and unstructured data. The information sourcesare the source of creating new ontologies combining them, etc. Theinformation retrieval engine is based on the categorization of the data.

Ontology is also used for query expansion. In U.S. Pat. No. 5,822,731(“the '731 patent”), the contents of which is incorporated herein byreference in its entirety, a semantic network is applied to maximize thenumber of relevant documents identified during a query search bysemantically expanding the search in response to the part of speechassociated with each query term in the search.

In U.S. patent application Publication No. 2001/0003183 A1, (“the '183publication”), the contents of which is incorporated herein by referencein its entirety, a method and apparatus for knowledgebase searching isdescribed. Ontologies are an integral part of this system. A library ofquery templates and a dictionary that relates keywords to more abstractconcepts are first prepared on a computer system. Each template containsone or more typed variables. A query is then generated by entering intothe system one or more keywords. Each keyword is abstracted to concept(using different thesauri and ontologies). Each concept may be furtherrefined by additional abstraction, or by picking one concept fromseveral candidates, or by successive abstraction and rejection ofdifferent keywords until an acceptable concept is found. Next, for theconcepts that are obtained, the system finds all query templates arethen instantiated with those concepts or with the keywords used to formthe concepts. The user then selects the most appropriate query fromamong the instantiated query templates. The system of the '183publication may be applied in formulating queries to access any set ofinformation sources. The '183 publication system is particularly usefulto access distributed, heterogeneous databases which do not have asingle standardized vocabulary or structure.

In fact, the latter three above-mentioned methods represent key wordsearch expansion by means of ontology with different variations.

The method and device for supporting information retrieval by usingontology, and storage medium recording information retrieval supportprogram, described in JP-2000222436, (“the '436 publication”), thecontents of which is incorporated herein by reference in its entirety,is designed to provide an information retrieval supporting method. Themethod is capable of dynamically preparing a database selection menu forselection of a database suited for retrieving information required by auser. The solution suggested by the author of the '436 publication isontology describing the concept system of information managed by adatabase as tree structure of information concepts from a higher degreeof abstraction to a lower degree of abstraction and a database selectionmenu for specifying the concept of information required to be retrievedby a user is dynamically generated by presenting concepts registered inthe ontology stepwise from the higher degree of abstraction to the lowerdegree of abstraction.

Briefly, the method of the '436 publication suggests using ontology,reflecting database content, to help a user to specify the concept thatis searched by refinement or generalization of the concept.

The method and system for query reformulation for searching ofinformation is described in U.S. patent application Publication No.20020147578 A1(“the '578 publication”), the contents of which isincorporated herein by reference in its entirety. The method providesreformulating the query by eliminating one or more non-interesting termsusing semantic and syntactic information for one or more of the terms;and querying a database of information based upon the reformulatedquery. Numerous interrelated dictionaries, thesauri and ontologies areused in the course of processing each question.

Hence, ontology in the system of the '578 publication is a part ofsystem that reformulates a query by eliminating non-informative terms.

Ontology is also applied in information retrieval systems to rank queryfeedback terms, as is described in U.S. Pat. No. 6,363,378 B1(“the '378patent”), the contents of which is incorporated herein by reference inits entirety. The information retrieval system processes the queries,identifies topics related to the query as well as query feedback terms,and then links both the topics and feedback terms to nodes of theknowledge base with corresponding terminological concepts. At least onefocal node is selected from the knowledge base based on the topics todetermine a conceptual proximity between the focal node and the queryfeedback nodes. Hierarchical relations from ontology are used tocalculate semantic proximity between focal categories and query feedbackterms. The query feedback terms are ranked based on conceptual proximityto the focal node.

Thus, in the '378 patent's information retrieval system, ontology isused for topic identification in the knowledge base and in the query andthen for calculating the semantic proximity between query feedback termsand the node chosen from the database on the basis of determined topic.

Therefore, the idea of using ontology to improve information retrievalsystem performance is not new and it is is disclosed in differentmanners in various patents. For example, some of the different mannersdisclosed include for searching in structured and unstructureddatabases, for document theme or topic identification, for normalizationof semantic representation of the string, for search and integration ofdifferent types of data, for query expansion, etc. As far as ontologyuse in query expansion is concerned, ontology is applied, generally, toexpand keyword-based and concept-based search and hierarchical relationsfrom ontology are preponderantly used in a certain knowledge domain.

SUMMARY OF THE INVENTION

An exemplary embodiment of the present invention may include a system,method and/or computer program product that may provide an ability tosolve a problem such as, e.g., but not limited to, inventor problems anduser problems, based on semantic methods for data/knowledge presentationand processing, implemented in a linguistic processing module. The basiccomponents of this module may be, in an exemplary embodiment, alinguistic knowledge base (KB), an ontology KB, and/or an expert KB.

The linguistic KB, according to an exemplary embodiment of the presentinvention, may provide a linguistic analysis of a user's query and itsformal semantic representation—verb-parameter-object (VPO), also called“a technical function,” which may be a formal specification of theproblem.

The ontology KB may contain knowledge of the surrounding world,presented in a number of terms (concepts and verbs) from differentknowledge domains and semantic relations binding these terms, examplesinclude: synonymic, “kind-of” and associative relations.

The linguistic processing module may perform semantic expansion withhelp of the ontology KB. The linguistic processing module may providefor a maximum recall and precision of information retrieval whensearching for solutions to a given problem or its analogs, which may bevery important when dealing with the mentioned class of tasks. Inaddition to this, a user may have the possibility of varying the degreeof the semantic expansion based on proximity of terms in the ontologyKB.

Expert KB, in an exemplary embodiment, may be a knowledge database ofsolutions for technical problems, obtained from numerous text documents,mainly from patents and articles. These solutions may be presented inSVPO format, where S may be a subject, or solver or performer of atechnical function defined by VPO. Comparing a semantically expandedquery and solutions from the expert KB, the linguistic processing modulemay locate the solutions (including analogous solutions) for the givenquery. The output of the linguistic processing module, in an exemplaryembodiment, may be a semantically sorted list of these solutions. As aresult, the user may be presented with a list of, e.g., precise,particular, general and analogous solutions for the query.

According to an exemplary embodiment of the present invention, thelinguistic processing module may provide an effective solution for auser's query by implementing linguistic, ontology and expert knowledgebases (KBs) and a set of tools to edit and otherwise enrich themtogether with semantic methods (based thereupon) forinformation/knowledge processing.

Ontology use may significantly improve the performance of informationretrieval systems, which deal with documents that are the maininformation-carrying medium:

-   -   if the correct semantic analysis of user query is provided;    -   if ontology is used for each of the main informative elements of        user query; or/and    -   if ontology reflects all the necessary knowledge domain concepts        and relations needed to solve the above-mentioned tasks.

Therefore, exemplary distinctive characteristics of our approach mayinclude the following:

1. A new method for solving problems such as, e.g, but not limited to,inventor problems and user problems, based on the linguistic processingof text documents (mainly for patents) may be provided;

2. By virtue of point 1, the linguistic processing module may provide:

-   -   a) a formal representation of the problem in the in the format        of Verb-Parameter Object (VPO);    -   b) an automatic semantic expansion of the formal representation        based on ontology;    -   c) an automatic semantic indexing of patent collections;    -   d) an automatic search of the exact solution in virtue of points        (2)(a) and (2)(b); and/or    -   e) an automatic search of the exact solution of more general,        more specific and an analogical problem.

3. The developed ontology may be universal from the point as:

-   -   a) the ontology can be applied in any knowledge domain;    -   b) the ontology reflects semantic relations for the main        semantic categories including concepts, their attributes and        verbs;    -   c) the ontology reflects main semantic relations between        semantic categories including:        -   main-attribute relations;        -   synonymous relations;        -   hierarchical relations; and        -   associative relations;    -   d) the ontology possesses the mechanism for regulating relation        depth between semantic categories; and/or    -   e) the ontology may be open for the user, i.e. the technology        for its editing may be provided.

In this way, the approach of the exemplary embodiment of the presentinvention, may actually provide effective support of professionalactivities of inventors and may facilitate the solving of problems of atypical user.

Further features and advantages of the invention, as well as thestructure and operation of various exemplary embodiments of theinvention, are described in detail below with reference to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the invention will beapparent from the following, more particular description of exemplaryembodiments of the invention, as illustrated in the accompanyingdrawings. In the drawings, like reference numbers generally indicateidentical, functionally similar, and/or structurally similar elements.The drawing in which an element first appears is indicated by theleftmost digits in the corresponding reference number. A preferredexemplary embodiment is discussed below in the detailed description ofthe following drawings:

FIG. 1 provides a structural and functional scheme of linguisticprocessing module for inventor and user problems solving according to anexemplary embodiment of the present invention;

FIG. 2 provides an example fragment of Ontology KB for conceptsaccording to an exemplary embodiment of the present invention; and

FIG. 3 provides structural and functional scheme of Expanding module ofLPM according to an exemplary embodiment of the present invention.

DETAILED DESCRIPTION OF THE VARIOUS EXEMPLARY EMBODIMENTS

Overview of Linguistic processing module for Inventor and User Problems

An exemplary embodiment of the present invention may provide a way ofsolving problems. In an exemplary embodiment, a linguistic processingmodule (LPM), with help of a multi-component knowledge base (KB) fornatural language and relations between entities of a certain area ofinterest, may provide high-quality understanding of structured andnon-structured user queries and a search technique for finding a mostexact and complete list of related solutions.

FIG. 1 depicts a diagram 100 illustrating a structural and functionalscheme of a linguistic processing module (LPM) for inventor and userproblem solving according to an exemplary embodiment of the presentinvention. The LPM of FIG. 1 may receive, as illustrated, a User Query104. Using Linguistic Knowledge Base 132, the LPM may process User Query(Unit 108) and may produce its formal presentation (Unit 112, notshown). Further, a semantic expansion may be performed for the givenquery (Unit 116) with use of an Ontology Knowledge Base 136 of LPM. Themultitude of search patterns obtained thereby may be fed to the SearchModule 120 of LPM. Using an Expert Knowledge Base 140, LPM may locateall available solutions (which may be stored as a number of searchpatterns) for the problem, may sort them according to relevance (Unit124) and may output the results for the user as a list of solutions(Unit 128).

The Linguistic KB 132 may contain, e.g., but not limited to, rules forparsing, a lemmatization dictionary, a linguistic algorithm for leftmostword trimming and for noun phrases canonization, in an exemplaryembodiment.

The Ontology KB 136 may be a hierarchical database of terms fordifferent knowledge domains. The “term” here is used to denote a concept(term-concept) and a verb (term-verb). Before the structure and contentsof Ontology KB is described, it is necessary to give the followingdefinitions:

Synonymy—the semantic relation that holds between two words or twogrammatical structures that can (in a given context) express the samemeaning.

For example: “alter”, “change”, “modify”, “vary”

Direct synonyms—words or grammatical structures the have the same (orclose) meaning regardless of the context.

For example: “water”, “aqua”

Syntactic synonyms—grammatical structures of different types expressingthe same (or close) meaning.

For example: “to heat”, “to increase temperature”

“Kind-of” relation (hypernymy/hyponymy)—semantic relation that holdsbetween two words or two grammatical structures where one of themrepresents a class of objects and the other is a particularrepresentative of the given class.

For example: “oxygen”→“gas”, “increase”→“change”,“temperature”→“parameter”

Association—semantic relation between two words or two grammaticalstructures with meanings that are in coordinate relations to each other.They are called “sisters/brothers” and have the same “parent” hypernymand are referred as “children” of this hypernym.

Verb-Parameter-Object (VPO)—formal specification of the problem; Verbhere denotes a technical function to be improved; Parameter (may beabsent, in which case we refer to VO only) denotes a specificcharacteristic of the technical system or an element of a technicalsystem to be improved; Object denotes a technical system or an elementof a technical system involved into the technical function or process.

For example:

-   Problem: How to increase temperature of water?-   VPO: V (increase) P (temperature) O (water)

Subject (S)—is a “solver” of a technical problem defined in VPOstructure.

For example:

-   Fire increases temperature of water-   S (fire) V (increase) P (temperature) O (water)

Main-word—a particular word within a noun-phrase, which definesgrammatical properties of the entire noun-phrase.

For example:

-   Noun-phrase: cold water-   Main-word: water

Lemmatization—the process of producing the initial form of a word fromits word-forms. The initial form for verbs is infinitive form of theverb, for nouns—noun in Nominative case, singular number.

For example:

-   verb: “moving”→“(to) move”-   noun: “cars”→“car”

Synset—a set of synonymic concepts (either nouns or verbs).

For example:

-   Synset: “marine vessel”, “vessel”, “watercraft”

Synonym expansion—a function that takes a word (or more complexgrammatical element, such as VPO) and returns a set of grammaticalelements which express the same meaning.

For example:

-   “vessel”—is expanded into—“marine vessel”, “watercraft”-   “(to) heat”—is expanded into—“increase temperature”

“Kind-of” expansion—a function that takes a word (or more complexgrammatical element, such as VPO) and returns a set of grammaticalelements which express more general or more specific meaning.

For example:

-   “marine vessel”—is expanded into—“craft” (general meaning)-   “marine vessel”—is expanded into—“ice yacht”, “patrol boat”,    “scooter”, etc . . . (specific meanings)

“Association” expansion—a function that takes a word (or more complexgrammatical element, such as VPO) and returns a set of grammaticalelements which express closely associated meanings.

For example:

-   “regularization”—is associated with—“regulation”, “quality control”,    “restraint”, etc . . .

The terms in Ontology KB are grouped on the basis of the followingrelations:

-   -   1) “synonymy”—relation, including:        -   1a) “direct synonym”—relation        -   1b) “syntactic synonym”—relation    -   2) “Kind-of”—relation (hypernym→hyponym)    -   3) “association”—relation

Besides, relations (1a), (2) and (3) are characteristic of term-conceptsand (1a), (1b) and (2) are characteristic of term-verbs. Relation oftype (1b) refers to synonyms in form of:

-   -   verb₁=verb₂+parameter,        for example:    -   moisten=augment humidity    -   heat=increase temperature

In order to enrich Ontology KB 136, a special computer-based toolsetaccording to the present invention was developed which automatized thework of domain knowledge experts (or lexicographers).

FIG. 2 depicts a tree diagram 200, showing an example illustrating anexcerpt from Ontology KB 136 of term-concepts. Every node of the tree200 (except nodes 244, 252, 260) contain terms, connected via relationof type (1a), while the arcs of the tree denote relation of type (2).Thus, any term from node 248 is a hyponym for the term from node 244.Eventually, any term from any node may be connected via relation of type(3) with any other term from another node of the same level. Forinstance, the only term from node 252 is in relation (3) with either ofthe terms from nodes 256, 260, 264.

Expert KB 140 (FIG. 1) is a knowledge base of technical solutionsextracted from NL documents (patents, articles, etc.). The expert KB 140may be used by the search module only. Each solution is presented by aNL sentence and by 4 pairs of fields, corresponding to the basicconcepts of S, V, P, O (Subject, Verb, Parameter, Object; further,SVPO). It should be noted that Subject fields are not directly used bythe search engine and are considered as a solution of the problem(defined by VPO fields).

A number of requirements must be met to correctly produce the VPO fieldsfor the search:

1. Every field must be presented in a canonized form:

-   -   Parameter and Object must contain noun phrases (or groups) in        Nominative case, singular number, for example:

-   “nanotube arrays”→“nanotube array”    -   if Parameter and/or Object contain of-phrases, they must be        converted into stone-wall constructions, for example:

-   “query of user”→“user query”    -   Verb fields must contain verb (and, optionally, a postposition)        in its infinitive form, for example:

-   “provided”→“provide”

2. Parameter or Object fields with two or more terms bound with help ofa conjunction are split into several parts at the point(s) ofconjunction(s), thus forming 2 or more Parameters or Objects, forexample:

-   “polymers and copolymers”→“polymer”, “copolymer”

3. Parameter and/or Object fields contain simple noun phrases, alladditional information is stripped:

-   “bowl containing water”→Object: “bowl”

An example of a technical solution is shown below:

NL Format:

Accelerometer detects acceleration of magnetic head

SVPO Format:

S: accelerometer

V: detect

P: acceleration

O: magnetic head

The Linguistic Processing Module (FIG. 1) works using the describedabove knowledge bases (Linguistic KB 132, Ontology KB 136, Expert KB140). User Query 104 may be provided to the LPM. Initially the work ofLPM may be focused on the validation of the user's query. VPO fields aremandatory for the query structure. The idea is that, as the researchshowed, the majority of invention problems could be presented in form ofa so-called “technical function”, that is, in VPO form, which is aformal specification of a problem (for example: “disk increases thedepth of grinding”, where V is “increases”, P is “depth”, O is“grinding”), while the solution of the problem lies in locating theperformer of this technical function. As the term suggests, structured(VPO) functional query is not to be linguistically processed. Thenon-structured functional query undergoes linguistic processing in orderto obtain VPO fields. LPM uses a few rules for linguistic processing(Unit 108) which are used to describe a Top-Down Parsing model. Theprocessing algorithm does not require a lot of resources to beimplemented. It appears to be quite efficient for processingnon-structured user's query, given the following restrictions:

-   -   absence of Subject, and    -   more sophisticated sentence structure, than that which can be        found in most written texts, since all input sentences are using        Imperative Mood.

Below you can find examples of non-structured user queries and theresults of their linguistic processing.

EXAMPLE 1

-   Query: How to test fatigued metals?-   Structured form: V (test) O (fatigued metal)

EXAMPLE 2

-   Query: How to measure mechanical properties of MEMS material?-   Structured form: V (measure) P (mechanical property) O (MEMS    material)

It should be noted, that together with Linguistic KB (Unit 132) the LPMmay use Ontology KB 136 during processing, which may provide terms fornoun and verb phrases and, eventually, may increase the performance ofprocessing. A parsed user's query may be a formalized VPO structure.These fields must meet the same canonization requirements as those,specified for Expert KB 140.

User Query 104 in VPO format may be further submitted to Query Expansionmodule (Unit 116), which may make use of the hierarchical structure ofOntology KB 136 to perform semantic term expansion. This procedure maybe needed later, in order to retrieve as many problem-related solutionsas possible, using Expert KB 140.

FIG. 3 depicts diagram 300 illustrating a structural and functionalscheme of expanding module of LPM according to an exemplary embodimentof the present invention. Diagram 300 illustrates that a user query inVPO format 368 may be expanded using any of various exemplaryexpansions. Any of the following expansions may be implementedcorrespondingly (as illustrated in FIG. 3):

-   -   synonym expansion 372 (performed for verbs, parameters and        objects);    -   “kind-of” expansion 376 (hypernymic-hyponymic expansion,        performed only for objects); and/or    -   associative expansion 380 (performed only for objects).

The synonym expansion (Unit 372) may be a case when each field of userquery (in VPO format) may be substituted with its corresponding synonym:direct and syntactic synonyms.

For example:

-   Input (user's query): change dimensions of a solid body-   VPO format: V (change) P (dimension) O (body)-   Output (synonym expansion): V (change, alter, modify, vary) P    (dimension, proportion, size) O (body, organic structure, physical    structure)

It should be noted that in case of syntactical synonyms (V→VP or VP→V),the resulting terms may also be expanded to obtain synonymic terms.

“Kind-of” expansion (Unit 376) is a case when each field may besubstituted with the terms being in hierarchic relations (hypernym vs.hyponym) to the items specified in the query. “Kind-of” expansion maydepend on the term being specified in the query and on the semanticpossibilities of Ontology KB 136 to expand this term. There may be twotypes of “kind-of” expansions:

-   -   from a particular term to a general one (bottom-to-top);

for example:

-   Input (user's query): change the surface curvature of the conducting    liquid drop-   VPO format: V (change) P (surface curvature) O (conducting liquid    drop)-   Output (hypernymic V (change) expansion, performed only for-   Object): P (surface curvature) O (round shape, small indefinite    amount)    and,    -   from a general term to a particular one (top-to-bottom);

for example:

-   Input (user's query): change the direction of movement of the    gasflow-   VPO format: V (change) P (direction) O (movement)-   Output (hyponymic expansion, V (change)-   performed only for Object): P (direction) O (abduction, adduction,    flit, dart, circumduction, inclination, retraction, retrofection,    rotation, vibration

“Kind-of” relation 376 may give the possibility to examine moreparticular, more general and associated solutions.

Associative expansion (Unit 380) may be a case when each field may besubstituted with the terms in associative relations to the itemsspecified in the query.

For example:

-   Input (user's query): measure traveling distance-   VPO format: V (measure) O (traveling distance)-   Output (associative expansion, V (measure)-   performed only for Object): P(−) O(light time, skip distance,    wingspan, wingspread, object distance, migration distance, migration    length, altitude . . . )

Expansion of the associative kind 380 may allow one to examine thesolutions for similar problems (analogous solutions). Thus an expandeduser query in VPO format 384 may result as shown.

The goal of Searching for Solutions module (Unit 120) is to look for thesolutions in Expert KB (Unit 140) according to the obtained expansionfrom Query Expanding module (Unit 116) and consequently, present a listof solutions 128. The search engine may compare the VPO fields fromExpert KB 140 with the obtained expansions 372, 376, 384 from QueryExpanding module 116, 300. The correspondence of these fields may resultin retrieval of pertinent solutions according to the user's query 104.

The solutions in Expert KB 140 which may coincide with expansions interms of concepts and verbs to a certain degree, may be extracted andmay be put into the list of solutions 128 to be presented as a result tothe user. Due to the nature of those solutions they may need to besemantically sorted (in accordance with the types of expansions 372,376, 380). Sorting of Solutions module (Unit 124) may sort all recordsfrom Expert KB 140 that were marked for output in the following order:

-   -   1. Precise solutions. These are the solutions in which VO or VPO        fields may exactly coincide with those initially formulated in        the query.

For example: User's query: V (heat) O (water) Solution: S (coil) V(increase) P (temperature) O (water)

-   -   2. Particular solutions. These are the solutions where at least        one field out of VO or VPO fields may present more specific        terms than those formed in the query.

For example: User's query: V (neutralize) O (acid) Solution: S (alkali)V (neutralize) O (hydrochloric acid)

-   -   3. General solutions. These are the solutions in which at least        one of the fields from VO or VPO may be a generalization of the        term formulated in the query.

For example: User's query: V (neutralize) O (hydrochloric acid)Solution: S (alkali) V (neutralize) O (acid)

-   -   4. Analogous solutions. These are the solutions where at least        one of the fields from VO or VPO may represent an associated        term with those formulated in the query.

For example: User's query: V (neutralize) O (hydrochloric acid)Solution: S (alkali) V (neutralize) O (nitric acid)

In the examples above S stands for “a subject” or a Solver of theproblem. The algorithm for sorting the solutions according to their typecan be presented in the following two tables (for VPO and VO formats,respectively). A number of symbols used in the table should be describedbeforehand: S—original term or its synonym, H—hyponyms, R—hypernyms,C—associated terms, Exact—exact matching of terms, Partial—partialmatching (according to Leftmost Word Trimming algorithm), Any—Exact orPartial match. TABLE 1 Solution Parameter type Verb (V) (P) Object (O)Extra condition Precise S-Exact S-Exact S-Exact Particular S-ExactS-Exact SH-Exact O ∈ H-Exact General (1) S-Exact S-Exact SHR-Exact O ∈R-Exact General (2) S-Exact S-Any SHR-Any P ∈ S-Any & O ∈ SHR-PartialAnalogous S-Exact S-Any SHRC-Any O ∈ C-Any

TABLE 2 Solution Parameter type Verb (V) (P) Object (O) Extra conditionPrecise S-Exact — S-Exact Particular S-Exact — SH-Exact O ∈ H-ExactGeneral (1) S-Exact — SHR-Exact O ∈ R-Exact General (2) S-Exact —SHR-Any O ∈ SHR-Partial Analogous S-Exact — SHRC-Any O ∈ C-Any

It is deemed necessary to comment on these tables. Let us take, forexample, row “Analogous” from Table 1: column “Verb” says “S-Exact”,which means that the Verb field can contain a synonym (S) for theincoming (non-trimmed (Exact)) Verb. The same is relevant to Parameterfield, except that the synonyms can be found for a trimmed ornon-trimmed Parameter. Also, the Object field may contain any termobtained via semantic expansion (SHRC-Any). Eventually, “Extracondition” column says, that Object (trimmed or not, that is, “Any”)must contain an associated term (C).

As it is seen, there are two (2) “General” rows in the table. General(1) refers to those solutions, obtained only from semantic expansions ofnon-trimmed original term. General (2) refers to those solutions,obtained with use of Leftmost Word Trimming algorithm.

The idea of Leftmost Word Trimming algorithm is as follows. In case theexact match for the incoming term is not found in Ontology KB, theleftmost word is deleted and the remaining part of the term is againsought for in the Ontology KB. This process is repeated until the matchis found or there is only one word from the original term remains. Ineither case, the trimmed versions of the original term are referred toas having more general meaning, as compared to the complete, non-trimmedoriginal term. For example: “photosensitive resin composition”—istrimmed to—“resin composition”—is trimmed to—“composition”.

A computer and/or communications system may be used for severalcomponents of the system in an exemplary embodiment of the presentinvention. An exemplary embodiment may include a computer as may be usedfor several computing devices such as, e.g., but not limited to, theknowledge bases of the exemplary embodiments of the present invention.The computer may include, but is not limited to: e.g., any computerdevice, or communications device including, e.g., a personal computer(PC), a workstation, a mobile device, a phone, a handheld PC, a personaldigital assistant (PDA), a thin client, a fat client, a networkappliance, an Internet browser, a paging, or alert device, a television,an interactive television, a receiver, a tuner, a high definition (HD)television, an HD receiver, a video-on-demand (VOD) system, a server, orother device.

The computer, in an exemplary embodiment, may include a centralprocessing unit (CPU) or processor, which may be coupled to a bus.Processor may, e.g., access main memory via bus. The computer may becoupled to an Input/Output (I/O) subsystem such as, e.g., a networkinterface card (NIC), or a modem for access to a network. Computer mayalso be coupled to a secondary memory directly via bus, or via mainmemory, for example. Secondary memory may include, e.g., a disk storageunit or other storage medium. Exemplary disk storage units may include,but are not limited to, a magnetic storage device such as, e.g., a harddisk, an optical storage device such as, e.g., a write once read many(WORM) drive, or a compact disc (CD), or a magneto optical device.Another type of secondary memory may include a removable disk storagedevice, which may be used in conjunction with a removable storagemedium, such as, e.g. a CD-ROM, or a floppy diskette. In general, thedisk storage unit may store an application program for operating thecomputer system referred to commonly as an operating system. The diskstorage unit may also store documents of a database (not shown). Thecomputer may interact with the I/O subsystems and disk storage unit viabus. The bus may also be coupled to a display for output, and inputdevices such as, but not limited to, a keyboard and a mouse or otherpointing/selection device.

In this document, the terms “computer program medium” and “computerreadable medium” may be used to generally refer to media such as, e.g.,but not limited to removable storage drive, a hard disk installed inhard disk drive, and signals, etc. These computer program products mayprovide software to computer system. The invention may be directed tosuch computer program products.

References to “one embodiment,” “an embodiment,” “example embodiment,”“exemplary embodiments,” “various embodiments,” etc., may indicate thatthe embodiment(s) of the invention so described may include a particularfeature, structure, or characteristic, but not every embodimentnecessarily includes the particular feature, structure, orcharacteristic. Further, repeated use of the phrase “in one embodiment,”or “in an exemplary embodiment,” do not necessarily refer to the sameembodiment, although they may.

In the following description and claims, the terms “coupled” and“connected,” along with their derivatives, may be used. It should beunderstood that these terms are not intended as synonyms for each other.Rather, in particular embodiments, “connected” may be used to indicatethat two or more elements are in direct physical or electrical contactwith each other. “Coupled” may mean that two or more elements are indirect physical or electrical contact. However, “coupled” may also meanthat two or more elements are not in direct contact with each other, butyet still co-operate or interact with each other.

An algorithm is here, and generally, considered to be a self-consistentsequence of acts or operations leading to a desired result. Theseinclude physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated. It has proven convenient at times,principally for reasons of common usage, to refer to these signals asbits, values, elements, symbols, characters, terms, numbers or the like.It should be understood, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing,” “computing,”“calculating,” “determining,” or the like, refer to the action and/orprocesses of a computer or computing system, or similar electroniccomputing device, that manipulate and/or transform data represented asphysical, such as electronic, quantities within the computing system'sregisters and/or memories into other data similarly represented asphysical quantities within the computing system's memories, registers orother such information storage, transmission or display devices.

In a similar manner, the term “processor” may refer to any device orportion of a device that processes electronic data from registers and/ormemory to transform that electronic data into other electronic data thatmay be stored in registers and/or memory. A “computing platform” maycomprise one or more processors.

Embodiments of the present invention may include apparatuses forperforming the operations herein. An apparatus may be speciallyconstructed for the desired purposes, or it may comprise a generalpurpose device selectively activated or reconfigured by a program storedin the device.

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. Thus, the breadth and scope of thepresent invention should not be limited by any of the above-describedexemplary embodiments, but should be defined only in accordance with thefollowing claims and their equivalents. While this invention has beenparticularly described and illustrated with reference to a preferredembodiment, it will be understood to those having ordinary skill in theart that changes in the above description or illustrations may be madewith respect to formal detail without departing from the spirit andscope of the invention.

1. A method for solving a problem comprising: storing a user query whichcomprises at least one of a structured or a non-structured descriptionof the problem; parsing of said user query wherein said user query isnon-structured, to create a structured user query, wherein saidstructured user query comprises a formal semantic representation of saiduser query; semantically expanding said formal semantic representationof said user query to provide searching for at least one solutionrelevant to the problem; searching said at least one solution in anExpert Knowledge Base; and semantically sorting said at least onesolution to produce a list of at least one solution for the problem. 2.The method of claim 1, wherein said user query comprises a structuredquery, is presented in semantic verb parameter object (VPO) format. 3.The method of claim 1, wherein said problem comprises at least one of aninventor problem, or a user problem.
 4. The method of claim 1, whereinsaid non-structured user query is presented in a natural languageformat.
 5. The method according to claim 1, wherein said semanticallyexpanding comprises searching for all relevant of said at least onesolution relevant to the problem.
 6. The method of claim 1, wherein saidparsing step comprises: lexically and grammatically analyzing saidnon-structured user query; and semantically analyzing saidnon-structured query to create said formal semantic representation ofsaid user query.
 7. The method of claim 6, wherein said lexically andgrammatically analyzing is based on at least one of a LinguisticKnowledge Base, or an Ontology Knowledge Base to recognize noun and verbphrases of said user query.
 8. The method of claim 6, wherein saidsemantically analyzing is based on lexically and grammatically analyzingsaid user query and lemmatization dictionary and linguistic algorithmsfor noun and verb phrases canonization and list of parameters from aLinguistic Knowledge Base to produce said formal semantic representationof said user query in a verb parameter object (VPO) format.
 9. Themethod of claim 1, wherein said semantically expanding is based onexpanding based on an Ontology Knowledge Base producing a semanticexpansion of said formal semantic representation of said user query. 10.The method of claim 9, wherein said Ontology Knowledge Base comprises adatabase of at least one of a term, or a relation between two or more ofsaid at least one terms for different knowledge domains.
 11. The methodof claim 10, wherein said term comprises at least one term-concept, orterm-verb.
 12. The method of claim 10, wherein said relation for aterm-verb comprises at least one of a direct synonym, or a syntacticsynonym.
 13. The method of claim 10, wherein said relation forterm-concepts comprises at least one of adirect synonym, a kind-of, oran association.
 14. The method of claim 9, wherein said OntologyKnowledge Base comprises at least one of technology, or tools for domainknowledge experts to at least one of edit, or enrich said OntologyKnowledge Base.
 15. The method of claim 9, wherein said semanticexpansion comprises a list of search patterns relevant to said formalsemantic representation of said user query.
 16. The method of claim 15,wherein every search pattern from said list of patterns includes itsrelevance value of said every search pattern compared to said formalsemantic representation of user query.
 17. The method of claim 1,wherein said searching comprises searching based on a list of at leastone search pattern and said Expert Knowledge Base to locate allavailable of said solutions of the problem.
 18. The method of claim 17,wherein said Expert Knowledge Base is a knowledge base of technicalsolutions extracted from a natural language document.
 19. The method ofclaim 18, wherein said natural language document comprises at least oneof a patent or an article.
 20. The method of claim 18, wherein saidtechnical solutions are presented in natural language sentences and insubject verb parameter object (SVPO) format.
 21. The method of claim 17,wherein said Expert Knowledge Base is provided with technology and toolsfor domain knowledge experts to edit and enrich said Expert KnowledgeBase.
 22. The method of claim 17, wherein said locating of all availableof said solutions is performed only for those technical solutions fromsaid Expert Knowledge Base, where V, P and O fields match theappropriate fields of the search patterns.
 23. The method of claim 22,wherein said technical solutions located are given similar relevancevalues to relevance values of the search patterns.
 24. The method ofclaim 1, wherein said semantically sorting comprises producing a list ofsolutions based on types of semantic expansion and relevance values oftechnical solutions of the problem.
 25. The method of claim 24, whereinsaid list of solutions is first sorted on the basis of type of semanticexpansion in the following order: precise solutions, particularsolutions, general solutions and analogous solutions.
 26. The method ofclaim 25, wherein said list of solutions inside of every sublist ofsolutions obtained in accordance with said type of semantic expansion,is further sorted according to the relevance values of said technicalsolutions.
 27. A system for solving a problem comprising: storing meansfor storing a user query which comprises at least one of a structured ora non-structured description of the problem; parsing means for parsingof said non-structured description of said user query to create saidstructured description of said user query, wherein said user querycomprises a formal semantic representation of said user query;semantically expanding means for semantically expanding said formalsemantic representation of said user query to provide searching for atleast one solution relevant to the problem; searching means forsearching said at least one solution in an Expert Knowledge Base; andsemantically sorting means for semantically sorting said at least onesolution to produce a list of solutions for the problem.
 28. The systemof claim 27, wherein the problem comprises at least one of an inventorproblem, or a user problem.
 29. The system of claim 27, wherein saidsemantically expanding means comprises searching for all relevant ofsaid at least one solution relevant to the problem.
 30. The system ofclaim 27, wherein said parsing means comprises: means for lexically andgrammatically analyzing said non-structured user query; and means forsemantically analyzing said non-structured query to create said formalsemantic representation of said user query.
 31. The system of claim 27,wherein said semantically expanding means is based on an OntologyKnowledge Base to produce a semantic expansion of said formal semanticrepresentation of said user query.
 32. The system of claim 31, whereinsaid Ontology Knowledge Base comprises a database of at least one of atleast one term, or a relation between two or more of said at least oneterms for different knowledge domains.
 33. The system of claim 27,wherein said searching means comprises searching based on a list of atleast one search pattern and said Expert Knowledge Base to locate allavailable of said solutions of the problem.
 34. The system of claim 33,wherein said Expert Knowledge Base comprises a knowledge base oftechnical solutions extracted from a natural language document.
 35. Thesystem of claim 27, wherein said semantically sorting means comprisesmeans for producing a list of solutions based on type of semanticexpansion and relevance values of technical solutions of the problem.36. The system of claim 35, wherein said list of solutions comprisesmeans for first sorting on the basis of type of semantic expansion inthe following order: precise solutions, particular solutions, generalsolutions and analogous solutions.
 37. The system of claim 36, whereinsaid list of solutions inside of every sublist of solutions obtained inaccordance with type of semantic expansion, is further sorted accordingto the relevance values of said technical solutions.
 38. A machinereadable media that provides instructions, which when executed by acomputing platform, cause said computing platform to perform operationscomprising a method of solving problems comprising: storing a user querycomprising at least one of a structured or non-structured description ofthe problem in verb parameter object (VPO) format; parsing saidnon-structured description of said user query to create said structureddescription of said user query wherein said structured description ofsaid user query comprises a formal semantic representation of said userquery; semantically expanding said formal semantic representation ofsaid user query to provide searching for at least one solution relevantto the problem; searching said at least one solution in an ExpertKnowledge Base; and semantically sorting said at least one solution toproduce a list of solutions for the problem.